‘RL scaling’ tag

See Also
Gwern
Links
Miscellaneous
Bibliography

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See Also

Gwern

“Research Ideas”, Gwern 2017

Research Ideas

“It Looks Like You’re Trying To Take Over The World”, Gwern 2022

It Looks Like You’re Trying To Take Over The World

“The Scaling Hypothesis”, Gwern 2020

The Scaling Hypothesis

“Why Tool AIs Want to Be Agent AIs”, Gwern 2016

Why Tool AIs Want to Be Agent AIs

Links

“Data Scaling Laws in Imitation Learning for Robotic Manipulation”, Lin et al 2024

Data Scaling Laws in Imitation Learning for Robotic Manipulation

“AI Alignment via Slow Substrates: Early Empirical Results With StarCraft II”, Leong 2024

AI Alignment via Slow Substrates: Early Empirical Results With StarCraft II⁠:

View HTML:

/doc/www/www.greaterwrong.com/44c0ed1a28a38215d6f2d43ed0b4d4ad3b0f38e4.html

“MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering”, Chan et al 2024

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

“NAVIX: Scaling MiniGrid Environments With JAX”, Pignatelli et al 2024

NAVIX: Scaling MiniGrid Environments with JAX

“JEST: Data Curation via Joint Example Selection Further Accelerates Multimodal Learning”, Evans et al 2024

JEST: Data curation via joint example selection further accelerates multimodal learning

“AI Search: The Bitter-Er Lesson”, McLaughlin 2024

AI Search: The Bitter-er Lesson

“Foundational Challenges in Assuring Alignment and Safety of Large Language Models”, Anwar et al 2024

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

“Simple and Scalable Strategies to Continually Pre-Train Large Language Models”, Ibrahim et al 2024

Simple and Scalable Strategies to Continually Pre-train Large Language Models

“Robust Agents Learn Causal World Models”, Richens & Everitt 2024

Robust agents learn causal world models

“Grandmaster-Level Chess Without Search”, Ruoss et al 2024

Grandmaster-Level Chess Without Search

“Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training”, Hubinger et al 2024

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

“Vision-Language Models As a Source of Rewards”, Baumli et al 2023

Vision-Language Models as a Source of Rewards

“JaxMARL: Multi-Agent RL Environments in JAX”, Rutherford et al 2023

JaxMARL: Multi-Agent RL Environments in JAX

“Diversifying AI: Towards Creative Chess With AlphaZero (AZ_db)”, Zahavy et al 2023

Diversifying AI: Towards Creative Chess with AlphaZero (AZ_db)

“Deep RL at Scale: Sorting Waste in Office Buildings With a Fleet of Mobile Manipulators”, Herzog et al 2023

Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators

“Emergence of Belief-Like Representations through Reinforcement Learning”, Hennig et al 2023

Emergence of belief-like representations through reinforcement learning

“Scaling Laws for Single-Agent Reinforcement Learning”, Hilton et al 2023

Scaling laws for single-agent reinforcement learning

“DreamerV3: Mastering Diverse Domains through World Models”, Hafner et al 2023

DreamerV3: Mastering Diverse Domains through World Models

“Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes”, Kumar et al 2022

Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes

“VeLO: Training Versatile Learned Optimizers by Scaling Up”, Metz et al 2022

VeLO: Training Versatile Learned Optimizers by Scaling Up

“Broken Neural Scaling Laws”, Caballero et al 2022

Broken Neural Scaling Laws

“Scaling Laws for Reward Model Overoptimization”, Gao et al 2022

Scaling Laws for Reward Model Overoptimization

“SAP: Bidirectional Language Models Are Also Few-Shot Learners”, Patel et al 2022

SAP: Bidirectional Language Models Are Also Few-shot Learners

“`g.pt`: Learning to Learn With Generative Models of Neural Network Checkpoints”, Peebles et al 2022

g.pt: Learning to Learn with Generative Models of Neural Network Checkpoints

“Human-Level Atari 200× Faster”, Kapturowski et al 2022

Human-level Atari 200× faster

“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Ganguli et al 2022

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

“AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, Soltan et al 2022

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

“TextWorldExpress: Simulating Text Games at One Million Steps Per Second”, Jansen & Côté 2022

TextWorldExpress: Simulating Text Games at One Million Steps Per Second

“Demis Hassabis: DeepMind—AI, Superintelligence & the Future of Humanity § Turing Test”, Hassabis & Fridman 2022

Demis Hassabis: DeepMind—AI, Superintelligence & the Future of Humanity § Turing Test

“Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”, Baker et al 2022

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

“Multi-Game Decision Transformers”, Lee et al 2022

Multi-Game Decision Transformers

“Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline (3RL)”, Caccia et al 2022

Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline (3RL)

“CT0: Fine-Tuned Language Models Are Continual Learners”, Scialom et al 2022

CT0: Fine-tuned Language Models are Continual Learners

“Flexible Diffusion Modeling of Long Videos”, Harvey et al 2022

Flexible Diffusion Modeling of Long Videos

“Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Honovich et al 2022

Instruction Induction: From Few Examples to Natural Language Task Descriptions

“Gato: A Generalist Agent”, Reed et al 2022

Gato: A Generalist Agent

“Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”, Chan et al 2022

Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers

“Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale”, Ramrakhya et al 2022

Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale

“Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”, Ahn et al 2022

Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances

“Socratic Models: Composing Zero-Shot Multimodal Reasoning With Language”, Zeng et al 2022

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

“InstructGPT: Training Language Models to Follow Instructions With Human Feedback”, Ouyang et al 2022

InstructGPT: Training language models to follow instructions with human feedback

“A Data-Driven Approach for Learning to Control Computers”, Humphreys et al 2022

A data-driven approach for learning to control computers

“EvoJAX: Hardware-Accelerated Neuroevolution”, Tang et al 2022

EvoJAX: Hardware-Accelerated Neuroevolution

“Accelerated Quality-Diversity for Robotics through Massive Parallelism”, Lim et al 2022

Accelerated Quality-Diversity for Robotics through Massive Parallelism

“Don’t Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning (ExORL)”, Yarats et al 2022

Don’t Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning (ExORL)

“Can Wikipedia Help Offline Reinforcement Learning?”, Reid et al 2022

Can Wikipedia Help Offline Reinforcement Learning?

“In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, Kurin et al 2022

In Defense of the Unitary Scalarization for Deep Multi-Task Learning

“The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models”, Pan et al 2022

The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models

“WebGPT: Browser-Assisted Question-Answering With Human Feedback”, Nakano et al 2021

WebGPT: Browser-assisted question-answering with human feedback

“WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton et al 2021

WebGPT: Improving the factual accuracy of language models through web browsing

“Acquisition of Chess Knowledge in AlphaZero”, McGrath et al 2021

Acquisition of Chess Knowledge in AlphaZero

“AW-Opt: Learning Robotic Skills With Imitation and Reinforcement at Scale”, Lu et al 2021

AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale

“An Explanation of In-Context Learning As Implicit Bayesian Inference”, Xie et al 2021

An Explanation of In-context Learning as Implicit Bayesian Inference

“Procedural Generalization by Planning With Self-Supervised World Models”, Anand et al 2021

Procedural Generalization by Planning with Self-Supervised World Models

“MetaICL: Learning to Learn In Context”, Min et al 2021

MetaICL: Learning to Learn In Context

“Collaborating With Humans without Human Data”, Strouse et al 2021

Collaborating with Humans without Human Data

“T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Sanh et al 2021

T0: Multitask Prompted Training Enables Zero-Shot Task Generalization

“Bridge Data: Boosting Generalization of Robotic Skills With Cross-Domain Datasets”, Ebert et al 2021

Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets

“Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning”, Rudin et al 2021

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

“Recursively Summarizing Books With Human Feedback”, Wu et al 2021

Recursively Summarizing Books with Human Feedback

“FLAN: Finetuned Language Models Are Zero-Shot Learners”, Wei et al 2021

FLAN: Finetuned Language Models Are Zero-Shot Learners

“Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation”, Nair et al 2021

Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation

“WarpDrive: Extremely Fast End-To-End Deep Multi-Agent Reinforcement Learning on a GPU”, Lan et al 2021

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

“Multi-Task Self-Training for Learning General Representations”, Ghiasi et al 2021

Multi-Task Self-Training for Learning General Representations

“Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning”, Makoviychuk et al 2021

Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

“Open-Ended Learning Leads to Generally Capable Agents”, Team et al 2021

Open-Ended Learning Leads to Generally Capable Agents

“Megaverse: Simulating Embodied Agents at One Million Experiences per Second”, Petrenko et al 2021

Megaverse: Simulating Embodied Agents at One Million Experiences per Second

“Evaluating Large Language Models Trained on Code”, Chen et al 2021

Evaluating Large Language Models Trained on Code

“PES: Unbiased Gradient Estimation in Unrolled Computation Graphs With Persistent Evolution Strategies”, Vicol et al 2021

PES: Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies

“Multimodal Few-Shot Learning With Frozen Language Models”, Tsimpoukelli et al 2021

Multimodal Few-Shot Learning with Frozen Language Models

“Brax—A Differentiable Physics Engine for Large Scale Rigid Body Simulation”, Freeman et al 2021

Brax—A Differentiable Physics Engine for Large Scale Rigid Body Simulation

“PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World”, Zellers et al 2021

PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World

“From Motor Control to Team Play in Simulated Humanoid Football”, Liu et al 2021

From Motor Control to Team Play in Simulated Humanoid Football

“Reward Is Enough”, Silver et al 2021

Reward is enough

“Podracer Architectures for Scalable Reinforcement Learning”, Hessel et al 2021

Podracer architectures for scalable Reinforcement Learning

“MuZero Unplugged: Online and Offline Reinforcement Learning by Planning With a Learned Model”, Schrittwieser et al 2021

MuZero Unplugged: Online and Offline Reinforcement Learning by Planning with a Learned Model

“Scaling Scaling Laws With Board Games”, Jones 2021

Scaling Scaling Laws with Board Games

“Large Batch Simulation for Deep Reinforcement Learning”, Shacklett et al 2021

Large Batch Simulation for Deep Reinforcement Learning

“Stockfish and Lc0, Test at Different Number of Nodes”, Meloni 2021

Stockfish and Lc0, test at different number of nodes

“Training Larger Networks for Deep Reinforcement Learning”, Ota et al 2021

Training Larger Networks for Deep Reinforcement Learning

“Investment vs. Reward in a Competitive Knapsack Problem”, Neumann & Gros 2021

Investment vs. reward in a competitive knapsack problem

“NNUE: The Neural Network of the Stockfish Chess Engine”, Goucher 2021

NNUE: The neural network of the Stockfish chess engine

“Imitating Interactive Intelligence”, Abramson et al 2020

Imitating Interactive Intelligence

“Scaling down Deep Learning”, Greydanus 2020

Scaling down Deep Learning

“Understanding RL Vision: With Diverse Environments, We Can Analyze, Diagnose and Edit Deep Reinforcement Learning Models Using Attribution”, Hilton et al 2020

Understanding RL Vision: With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution

“Meta-Trained Agents Implement Bayes-Optimal Agents”, Mikulik et al 2020

Meta-trained agents implement Bayes-optimal agents

“Measuring Progress in Deep Reinforcement Learning Sample Efficiency”, Anonymous 2020

Measuring Progress in Deep Reinforcement Learning Sample Efficiency

“Learning to Summarize from Human Feedback”, Stiennon et al 2020

Learning to summarize from human feedback

“Measuring Hardware Overhang”, hippke 2020

Measuring hardware overhang

“Sample Factory: Egocentric 3D Control from Pixels at 100,000 FPS With Asynchronous Reinforcement Learning”, Petrenko et al 2020

Sample Factory: Egocentric 3D Control from Pixels at 100,000 FPS with Asynchronous Reinforcement Learning

“Real World Games Look Like Spinning Tops”, Czarnecki et al 2020

Real World Games Look Like Spinning Tops

“Agent57: Outperforming the Human Atari Benchmark”, Puigdomènech et al 2020

Agent57: Outperforming the human Atari benchmark

“Deep Neuroethology of a Virtual Rodent”, Merel et al 2020

Deep neuroethology of a virtual rodent

“Near-Perfect Point-Goal Navigation from 2.5 Billion Frames of Experience”, Wijmans & Kadian 2020

Near-perfect point-goal navigation from 2.5 billion frames of experience

“Procgen Benchmark: We’re Releasing Procgen Benchmark, 16 Simple-To-Use Procedurally-Generated Environments Which Provide a Direct Measure of How Quickly a Reinforcement Learning Agent Learns Generalizable Skills”, Cobbe et al 2019

Procgen Benchmark: We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills

“DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames”, Wijmans et al 2019

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

“Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”, Vinyals et al 2019

Grandmaster level in StarCraft II using multi-agent reinforcement learning

“Solving Rubik’s Cube With a Robot Hand”, OpenAI et al 2019

Solving Rubik’s Cube with a Robot Hand

“Fine-Tuning Language Models from Human Preferences”, Ziegler et al 2019

Fine-Tuning Language Models from Human Preferences

“Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Baker et al 2019

Emergent Tool Use from Multi-Agent Interaction § Surprising behavior

“Emergent Tool Use from Multi-Agent Interaction § Surprising Behavior”, Baker et al 2019

Emergent Tool Use from Multi-Agent Interaction § Surprising behavior

“Meta Reinforcement Learning”, Weng 2019

Meta Reinforcement Learning

“Human-Level Performance in 3D Multiplayer Games With Population-Based Reinforcement Learning”, Jaderberg et al 2019

Human-level performance in 3D multiplayer games with population-based reinforcement learning

“AI-GAs: AI-Generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, Clune 2019

AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

“Meta-Learning of Sequential Strategies”, Ortega et al 2019

Meta-learning of Sequential Strategies

“Habitat: A Platform for Embodied AI Research”, Savva et al 2019

Habitat: A Platform for Embodied AI Research

“The Bitter Lesson”, Sutton 2019

The Bitter Lesson

“Benchmarking Classic and Learned Navigation in Complex 3D Environments”, Mishkin et al 2019

Benchmarking Classic and Learned Navigation in Complex 3D Environments

“Dota 2 With Large Scale Deep Reinforcement Learning: §4.3: Batch Size”, Berner 2019 (page 13)

Dota 2 with Large Scale Deep Reinforcement Learning: §4.3: Batch Size⁠:

View PDF:

/doc/www/arxiv.org/ce54be912a7192483c027c22c5fa8ff05e01ec77.pdf#page=13

“An Empirical Model of Large-Batch Training”, McCandlish et al 2018

An Empirical Model of Large-Batch Training

“How AI Training Scales”, McCandlish et al 2018

How AI Training Scales

“Bayesian Layers: A Module for Neural Network Uncertainty”, Tran et al 2018

Bayesian Layers: A Module for Neural Network Uncertainty

“Quantifying Generalization in Reinforcement Learning”, Cobbe et al 2018

Quantifying Generalization in Reinforcement Learning

“One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets With RL”, Paine et al 2018

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL

“Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias”, Gupta et al 2018

Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

“Human-Level Performance in First-Person Multiplayer Games With Population-Based Deep Reinforcement Learning”, Jaderberg et al 2018

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

“QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation”, Kalashnikov et al 2018

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

“Playing Atari With Six Neurons”, Cuccu et al 2018

Playing Atari with Six Neurons

“AI and Compute”, Amodei et al 2018

AI and Compute

“Accelerated Methods for Deep Reinforcement Learning”, Stooke & Abbeel 2018

Accelerated Methods for Deep Reinforcement Learning

“One Big Net For Everything”, Schmidhuber 2018

One Big Net For Everything

“Interactive Grounded Language Acquisition and Generalization in a 2D World”, Yu et al 2018

Interactive Grounded Language Acquisition and Generalization in a 2D World

“Emergence of Locomotion Behaviors in Rich Environments”, Heess et al 2017

Emergence of Locomotion behaviors in Rich Environments

“Deep Reinforcement Learning from Human Preferences”, Christiano et al 2017

Deep reinforcement learning from human preferences

“Evolution Strategies As a Scalable Alternative to Reinforcement Learning”, Salimans et al 2017

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

“On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, Schmidhuber 2015

On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models

“Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning”, Parisotto et al 2015

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

“Gorila: Massively Parallel Methods for Deep Reinforcement Learning”, Nair et al 2015

Gorila: Massively Parallel Methods for Deep Reinforcement Learning

“Algorithmic Progress in Six Domains”, Grace 2013

Algorithmic Progress in Six Domains

“Robot Predictions Evolution”, Moravec 2004

Robot Predictions Evolution

“When Will Computer Hardware Match the Human Brain?”, Moravec 1998

When will computer hardware match the human brain?

“Human Window on the World”, Michie 1985

Human Window on the World

“Time for AI to Cross the Human Performance Range in Chess”

Time for AI to cross the human performance range in chess

“Eric Jang”

Eric Jang⁠:

View HTML:

/doc/www/blog.evjang.com/af69140653b2ecee5f7a7cb4ccfc578dd3c79163.html

“Trading Off Compute in Training and Inference”

Trading Off Compute in Training and Inference

“Trading Off Compute in Training and Inference § MCTS Scaling”

Trading Off Compute in Training and Inference § MCTS scaling

“Submission #6347: Chef Stef’s NES Arkanoid `warpless` in 11:11.18”

Submission #6347: Chef Stef’s NES Arkanoid warpless in 11:11.18

“Training a CUDA TDS Ant Using C++ ARS Linear Policy: The Video Is Real-Time, After a Few Minutes (in the 30 Million Steps) the Training Curve Is Flat (I Trained Until a Billion Steps). Note That This Ant Is PD Control, and Not Identical to Either MuJoCo or PyBullet Ant, so the Training Curves Are Not Comparable Yet. Will Fix That.”

Training a CUDA TDS Ant using C++ ARS Linear policy: The video is real-time, after a few minutes (in the 30 million steps) the training curve is flat (I trained until a billion steps). Note that this Ant is PD control, and not identical to either MuJoCo or PyBullet Ant, so the training curves are not comparable yet. Will fix that.⁠:

https://www.youtube.com/watch?v=-MfUXSAehnw

“Ilya Sutskever: Deep Learning | AI Podcast #94 With Lex Fridman”

Ilya Sutskever: Deep Learning | AI Podcast #94 with Lex Fridman

“Target-Driven Visual Navigation in Indoor Scenes Using Deep Reinforcement Learning [Video]”

Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning [video]

“If You Want to Solve a Hard Problem in Reinforcement Learning, You Just Scale. It's Just Gonna Work Just like Supervised Learning. It's the Same, the Same Story Exactly. It Was Kind of Hard to Believe That Supervised Learning Can Do All Those Things, but It's Not Just Vision, It's Everything and the Same Thing Seems to Hold for Reinforcement Learning Provided You Have a Lot of Experience.”

If you want to solve a hard problem in reinforcement learning, you just scale. It's just gonna work just like supervised learning. it's the same, the same story exactly. It was kind of hard to believe that supervised learning can do all those things, but it's not just vision, it's everything and the same thing seems to hold for reinforcement learning provided you have a lot of experience.⁠:

https://www.youtube.com/watch?v=w3ues-NayAs?t=712#openai

Wikipedia

Moravec’s paradox

Miscellaneous

Bibliography

https://arxiv.org/abs/2410.07095#openai: “MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering”, Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Lilian Weng, Aleksander Madry

link-bibliography
https://yellow-apartment-148.notion.site/AI-Search-The-Bitter-er-Lesson-44c11acd27294f4495c3de778cd09c8d: “AI Search: The Bitter-Er Lesson”, Aidan McLaughlin

link-bibliography
https://arxiv.org/abs/2402.04494#deepmind: “Grandmaster-Level Chess Without Search”, Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Li Kevin Wenliang, Elliot Catt, John Reid, Tim Genewein

link-bibliography
https://arxiv.org/abs/2401.05566#anthropic: “Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training”, Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez

link-bibliography
https://arxiv.org/abs/2311.10090: “JaxMARL: Multi-Agent RL Environments in JAX”, Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Gardar Ingvarsson, Timon Willi, Akbir Khan, Christian Schroeder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert Tjarko Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktaschel, Chris Lu, Jakob Nicolaus Foerster

link-bibliography
https://arxiv.org/abs/2308.09175#deepmind: “Diversifying AI: Towards Creative Chess With AlphaZero (AZ_db)”, Tom Zahavy, Vivek Veeriah, Shaobo Hou, Kevin Waugh, Matthew Lai, Edouard Leurent, Nenad Tomasev, Lisa Schut, Demis Hassabis, Satinder Singh

link-bibliography
https://arxiv.org/abs/2301.04104#deepmind: “DreamerV3: Mastering Diverse Domains through World Models”, Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap

link-bibliography
https://arxiv.org/abs/2210.10760#openai: “Scaling Laws for Reward Model Overoptimization”, Leo Gao, John Schulman, Jacob Hilton

link-bibliography
https://arxiv.org/abs/2209.14500: “SAP: Bidirectional Language Models Are Also Few-Shot Learners”, Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch

link-bibliography
https://arxiv.org/abs/2209.12892: “g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints”, William Peebles, Ilija Radosavovic, Tim Brooks, Alexei A. Efros, Jitendra Malik

link-bibliography
https://arxiv.org/abs/2209.07550#deepmind: “Human-Level Atari 200× Faster”, Steven Kapturowski, Víctor Campos, Ray Jiang, Nemanja Rakićević, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia

link-bibliography
https://www.anthropic.com/red_teaming.pdf: “Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy L. Jones, Samuel R. Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El-Showk, Stanislav Fort, Zac Hatfield Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom Brown, Nicholas Joseph, Sam McCandlish, Chris Olah, Jared Kaplan, Jack Clark

link-bibliography
https://arxiv.org/abs/2208.01448#amazon: “AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, Rahul Gupta, Wael Hamza, Haidar Khan, Charith Peris, Stephen Rawls, Andy Rosenbaum, Anna Rumshisky, Chandana Satya Prakash, Mukund Sridhar, Fabian Triefenbach, Apurv Verma, Gokhan Tur, Prem Natarajan

link-bibliography
https://arxiv.org/abs/2206.11795#openai: “Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”, Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune

link-bibliography
https://arxiv.org/abs/2205.15241#google: “Multi-Game Decision Transformers”, Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch

link-bibliography
https://arxiv.org/abs/2205.12393: “CT0: Fine-Tuned Language Models Are Continual Learners”, Thomas Scialom, Tuhin Chakrabarty, Smaranda Muresan

link-bibliography
https://arxiv.org/abs/2205.06175#deepmind: “Gato: A Generalist Agent”, Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

link-bibliography
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